Differential Expression - Normal lung versus lung with distal tumor
2018-02-01
library(DESeq2)
library(bcbioRNASeq)
# Shared R Markdown settings
prepareRNASeqTemplate()
if (file.exists("setup.R")) {
source("setup.R")
}
# Directory paths
dataDir <- file.path(params$outputDir, "data")
countsDir <- file.path(params$outputDir, "results", "counts")
deDir <- file.path(params$outputDir, "results", "differential_expression")
# Load bcbioRNASeq object
bcbName <- load(params$bcbFile)
bcb <- get(bcbName, inherits = FALSE)Overview
- Principal Investigator:
- Researcher:
- Experiment:
> dds <- bcbio(bcb, "DESeqDataSet")
> design(dds) <- params$design
> dds <- DESeq(dds)
> rld <- rlog(dds)Alpha level (FDR) cutoffs
Let’s take a look at the number of genes we get with different false discovery rate (FDR) cutoffs. These tests subset P values that have been multiple test corrected using the Benjamini Hochberg (BH) method (Benjamini and Hochberg 1995).
> alphaSummary(dds)| 0.1 | 0.05 | 0.01 | 0.001 | 1e-06 | |
|---|---|---|---|---|---|
| LFC > 0 (up) | 449, 2.3% | 293, 1.5% | 98, 0.49% | 34, 0.17% | 6, 0.03% |
| LFC < 0 (down) | 260, 1.3% | 150, 0.75% | 36, 0.18% | 22, 0.11% | 5, 0.025% |
| outliers | 36, 0.18% | 36, 0.18% | 36, 0.18% | 36, 0.18% | 36, 0.18% |
| low counts | 6851, 34% | 8369, 42% | 9131, 46% | 0, 0% | 13698, 69% |
| cutoff | (mean count < 20) | (mean count < 41) | (mean count < 55) | (mean count < 0) | (mean count < 212) |
Results
> # help("results", "DESeq2")
> # For contrast argument as character vector:
> # 1. Design matrix factor of interest.
> # 2. Numerator for LFC (expt).
> # 3. Denominator for LFC (control).
> resUnshrunken <- results(
+ dds,
+ contrast = params$contrast,
+ alpha = params$alpha)
>
> # DESeqResults with shrunken log2 fold changes (LFC)
> # help("lfcShrink", "DESeq2")
> # Only `coef` or `contrast` can be specified, not both
> # Use the correct `coef` number to modify from `resultsNames(dds)`
> resShrunken <- lfcShrink(
+ dds = dds,
+ # coef = 2,
+ contrast = params$contrast,
+ res = resUnshrunken)
>
> # Use shrunken LFC values by default
> res <- resShrunken
> saveData(res, dir = dataDir)We performed the analysis using a BH adjusted P value cutoff of 0.05 and a log fold-change (LFC) ratio cutoff of 1.
Plots
Mean average (MA)
An MA plot compares transformed counts on M (log ratio) and A (mean average) scales (Y. H. Yang et al. 2002).
> plotMA(res)
Volcano
A volcano plot compares significance (BH-adjusted P value) against fold change (log2) (Cui and Churchill 2003; Li et al. 2014). Genes in the green box with text labels have an adjusted P value are likely to be the top candidate genes of interest.
> plotVolcano(res, lfc = params$lfc)
Heatmap
This plot shows only differentially expressed genes on a per-sample basis. We have scaled the data by row and used the ward.D2 method for clustering (Ward 1963).
> plotDEGHeatmap(res, counts = rld)
> top50res <- subset(res, padj < 0.05) %>% .[order(.$padj), ] %>% .[1:50, ]
> top50gene <- row.names(top50res)
>
> plotHeatmap(bcb, top50gene, normalized = "rlog")
File downloads
The results are saved as gzip-compressed comma separated values (CSV). Gzip compression is natively supported on macOS and Linux-based operating systems. If you’re running Windows, we recommend installing 7-Zip. CSV files can be opened in Excel or RStudio.
Count matrices
normalizedCounts.csv.gz: Use to evaluate individual genes and/or generate plots. These counts are normalized for the variation in sequencing depth across samples.tpm.csv.gz: Transcripts per million, scaled by length and also suitable for plotting.rawCounts.csv.gz: Only use to perform a new differential expression analysis. These counts will vary across samples due to differences in sequencing depth, and have not been normalized. Do not use this file for plotting genes.
Differentially expressed genes (DEG)
> resTbl <- resultsTables(res, lfc = params$lfc, write = TRUE, summary = TRUE,
+ headerLevel = 3, dir = deDir)Summary statistics
- 25168 genes in count matrix
- base mean > 0: 19949 genes (non-zero)
- base mean > 1: 17643 genes
- alpha cutoff: 0.05
- lfc cutoff: 1 (applied in tables only)
- deg pass alpha: 443 genes
- deg lfc up: 10 genes
- deg lfc down: 16 genes
DEG tables are sorted by BH-adjusted P value, and contain the following columns:
ensgene: Ensembl gene identifier.baseMean: Mean of the normalized counts per gene for all samples.log2FoldChange: log2 fold change.lfcSE: log2 standard error.stat: Wald statistic.pvalue: Walt test P value.padj: BH adjusted Wald test P value (corrected for multiple comparisons; aka FDR).externalGeneName: Ensembl name (a.k.a. symbol).description: Ensembl description.geneBiotype: Ensembl biotype (e.g.protein_coding).
Top tables
Only the top up- and down-regulated genes (arranged by log2 fold change) are shown.
> topTables(resTbl)| ensgene | baseMean | lfc | padj | symbol | description |
|---|---|---|---|---|---|
| ENSMUSG00000038418 | 452 | 1.50 | 6.86e-17 | Egr1 | early growth response 1 |
| ENSMUSG00000021250 | 400 | 1.69 | 9.26e-15 | Fos | FBJ osteosarcoma oncogene |
| ENSMUSG00000028859 | 254 | 1.57 | 8.82e-08 | Csf3r | colony stimulating factor 3 receptor (granulocyte) |
| ENSMUSG00000021134 | 1352 | 1.01 | 8.99e-08 | Srsf5 | serine/arginine-rich splicing factor 5 |
| ENSMUSG00000023034 | 410 | 1.31 | 1.80e-06 | Nr4a1 | nuclear receptor subfamily 4, group A, member 1 |
| ENSMUSG00000056054 | 111 | 1.19 | 1.64e-05 | S100a8 | S100 calcium binding protein A8 (calgranulin A) |
| ENSMUSG00000027398 | 94 | 1.37 | 8.09e-05 | Il1b | interleukin 1 beta |
| ENSMUSG00000070000 | 48 | 1.12 | 3.58e-04 | Fcho1 | FCH domain only 1 |
| ENSMUSG00000021624 | 53 | 1.08 | 2.39e-03 | Cd180 | CD180 antigen |
| ENSMUSG00000021209 | 124 | 1.07 | 4.15e-03 | Ppp4r4 | protein phosphatase 4, regulatory subunit 4 |
| ensgene | baseMean | lfc | padj | symbol | description |
|---|---|---|---|---|---|
| ENSMUSG00000097554 | 3743 | -1.30 | 5.81e-12 | Gm26825 | predicted gene, 26825 |
| ENSMUSG00000001774 | 287 | -1.15 | 1.44e-10 | Chordc1 | cysteine and histidine-rich domain (CHORD)-containing, zinc-binding protein 1 |
| ENSMUSG00000022206 | 1295 | -1.12 | 1.44e-10 | Npr3 | natriuretic peptide receptor 3 |
| ENSMUSG00000066687 | 1192 | -1.55 | 2.49e-10 | Zbtb16 | zinc finger and BTB domain containing 16 |
| ENSMUSG00000003949 | 665 | -1.07 | 7.05e-08 | Hlf | hepatic leukemia factor |
| ENSMUSG00000015656 | 2650 | -1.16 | 2.08e-07 | Hspa8 | heat shock protein 8 |
| ENSMUSG00000024966 | 510 | -1.25 | 3.63e-06 | Stip1 | stress-induced phosphoprotein 1 |
| ENSMUSG00000020288 | 200 | -1.05 | 2.02e-05 | Ahsa2 | AHA1, activator of heat shock protein ATPase 2 |
| ENSMUSG00000004951 | 1058 | -1.00 | 2.08e-05 | Hspb1 | heat shock protein 1 |
| ENSMUSG00000024222 | 201 | -1.27 | 3.29e-05 | Fkbp5 | FK506 binding protein 5 |
| ENSMUSG00000021131 | 100 | -1.06 | 5.31e-05 | Erh | enhancer of rudimentary homolog (Drosophila) |
| ENSMUSG00000028410 | 1389 | -1.12 | 5.31e-05 | Dnaja1 | DnaJ heat shock protein family (Hsp40) member A1 |
| ENSMUSG00000053930 | 67 | -1.15 | 3.58e-04 | Shisa6 | shisa family member 6 |
| ENSMUSG00000029657 | 889 | -1.03 | 2.84e-03 | Hsph1 | heat shock 105kDa/110kDa protein 1 |
| ENSMUSG00000060636 | 334 | -1.06 | 2.88e-03 | Rpl35a | ribosomal protein L35A |
| ENSMUSG00000050855 | 60 | -1.06 | 8.48e-03 | Zfp940 | zinc finger protein 940 |
Expression patterns
> top10res <- subset(res, padj < 0.05) %>% .[order(.$padj), ] %>% .[1:10, ]
> top10gene <- row.names(top10res)
>
> plots <- plotGene(bcb, top10gene, returnList = TRUE)
>
> n = 1
> cat(paste("##", plots[[n]]$labels$title))Egr1
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Fos
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Gm26825
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Chordc1
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Npr3
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Zbtb16
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Hlf
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Csf3r
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Srsf5
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Hspa8
> plots[[n]]
Methods
RNA-seq counts were generated by bcbio and bcbioRNASeq using salmon (Patro et al. 2017). Counts were imported into R using tximport (Soneson, Love, and Robinson 2016) and DESeq2 (Love, Huber, and Anders 2014). Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 (Wickham 2009). Heatmaps were generated by pheatmap (Kolde 2015).
R session information
> mdHeader("`devtools::session_info()`", level = 2)devtools::session_info()
> devtools::session_info()## setting value
## version R version 3.4.3 (2017-11-30)
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/New_York
## date 2018-02-01
##
## package * version date
## acepack 1.4.1 2016-10-29
## affy 1.56.0 2017-10-31
## affyio 1.48.0 2017-10-31
## annotate 1.56.1 2017-11-13
## AnnotationDbi 1.40.0 2017-10-31
## AnnotationFilter 1.2.0 2017-10-31
## AnnotationHub 2.10.1 2017-11-08
## assertthat 0.2.0 2017-04-11
## backports 1.1.2 2017-12-13
## base * 3.4.3 2017-12-07
## base64enc 0.1-3 2015-07-28
## basejump 0.2.0 2018-01-29
## bcbioBase * 0.0.3 2018-01-29
## bcbioRNASeq * 0.1.4 2018-01-30
## bindr 0.1 2016-11-13
## bindrcpp * 0.2 2017-06-17
## Biobase * 2.38.0 2017-10-31
## BiocGenerics * 0.24.0 2017-10-31
## BiocInstaller 1.28.0 2017-10-31
## BiocParallel 1.12.0 2017-10-31
## biomaRt 2.34.2 2018-01-20
## Biostrings 2.46.0 2017-10-31
## bit 1.1-12 2014-04-09
## bit64 0.9-7 2017-05-08
## bitops 1.0-6 2013-08-17
## blob 1.1.0 2017-06-17
## broom 0.4.3 2017-11-20
## cellranger 1.1.0 2016-07-27
## checkmate 1.8.5 2017-10-24
## circlize 0.4.3 2017-12-20
## cli 1.0.0 2017-11-05
## cluster 2.0.6 2017-03-10
## codetools 0.2-15 2016-10-05
## colorspace 1.3-2 2016-12-14
## compiler 3.4.3 2017-12-07
## ComplexHeatmap 1.17.1 2017-10-25
## ConsensusClusterPlus 1.42.0 2017-10-31
## cowplot 0.9.2 2017-12-17
## crayon 1.3.4 2017-09-16
## curl 3.1 2017-12-12
## data.table 1.10.4-3 2017-10-27
## datasets * 3.4.3 2017-12-07
## DBI 0.7 2017-06-18
## DEGreport * 1.14.1 2017-12-19
## DelayedArray * 0.4.1 2017-11-07
## dendsort 0.3.3 2015-12-14
## DESeq2 * 1.18.1 2017-11-12
## devtools 1.13.4 2017-11-09
## digest 0.6.15 2018-01-28
## dplyr * 0.7.4 2017-09-28
## edgeR 3.20.7 2018-01-18
## ensembldb 2.2.0 2017-10-31
## evaluate 0.10.1 2017-06-24
## forcats * 0.2.0 2017-01-23
## foreign 0.8-69 2017-06-22
## formatR 1.5 2017-04-25
## Formula 1.2-2 2017-07-10
## genefilter 1.60.0 2017-10-31
## geneplotter 1.56.0 2017-10-31
## GenomeInfoDb * 1.14.0 2017-10-31
## GenomeInfoDbData 1.0.0 2018-01-29
## GenomicAlignments 1.14.1 2017-11-18
## GenomicFeatures 1.30.1 2018-01-26
## GenomicRanges * 1.30.1 2017-12-21
## GetoptLong 0.1.6 2017-03-07
## ggplot2 * 2.2.1 2016-12-30
## ggrepel 0.7.0 2017-09-29
## GlobalOptions 0.0.12 2017-05-21
## glue 1.2.0 2017-10-29
## graphics * 3.4.3 2017-12-07
## grDevices * 3.4.3 2017-12-07
## grid 3.4.3 2017-12-07
## gridExtra 2.3 2017-09-09
## grr 0.9.5 2016-08-26
## gtable 0.2.0 2016-02-26
## haven 1.1.1 2018-01-18
## highr 0.6 2016-05-09
## Hmisc 4.1-1 2018-01-03
## hms 0.4.1 2018-01-24
## htmlTable 1.11.2 2018-01-20
## htmltools 0.3.6 2017-04-28
## htmlwidgets 1.0 2018-01-20
## httpuv 1.3.5 2017-07-04
## httr 1.3.1 2017-08-20
## interactiveDisplayBase 1.16.0 2017-10-31
## IRanges * 2.12.0 2017-10-31
## jsonlite 1.5 2017-06-01
## knitr * 1.19 2018-01-29
## labeling 0.3 2014-08-23
## lattice 0.20-35 2017-03-25
## latticeExtra 0.6-28 2016-02-09
## lazyeval 0.2.1 2017-10-29
## limma 3.34.6 2018-01-24
## locfit 1.5-9.1 2013-04-20
## logging 0.7-103 2013-04-12
## lubridate 1.7.1 2017-11-03
## magrittr 1.5 2014-11-22
## Matrix 1.2-12 2017-11-20
## Matrix.utils 0.9.6 2017-08-28
## MatrixModels 0.4-1 2015-08-22
## matrixStats * 0.53.0 2018-01-24
## memoise 1.1.0 2017-04-21
## methods * 3.4.3 2017-12-07
## mime 0.5 2016-07-07
## mnormt 1.5-5 2016-10-15
## modelr 0.1.1 2017-07-24
## munsell 0.4.3 2016-02-13
## nlme 3.1-131 2017-02-06
## nnet 7.3-12 2016-02-02
## Nozzle.R1 1.1-1 2013-05-15
## parallel * 3.4.3 2017-12-07
## pheatmap 1.0.8 2015-12-11
## pillar 1.1.0 2018-01-14
## pkgconfig 2.0.1 2017-03-21
## plyr 1.8.4 2016-06-08
## preprocessCore 1.40.0 2017-10-31
## prettyunits 1.0.2 2015-07-13
## progress 1.1.2 2016-12-14
## ProtGenerics 1.10.0 2017-10-31
## psych 1.7.8 2017-09-09
## purrr * 0.2.4 2017-10-18
## quantreg * 5.34 2017-10-25
## R.methodsS3 1.7.1 2016-02-16
## R.oo 1.21.0 2016-11-01
## R.utils 2.6.0 2017-11-05
## R6 2.2.2 2017-06-17
## RColorBrewer 1.1-2 2014-12-07
## Rcpp 0.12.15 2018-01-20
## RCurl 1.95-4.10 2018-01-04
## readr * 1.1.1 2017-05-16
## readxl 1.0.0 2017-04-18
## reshape 0.8.7 2017-08-06
## reshape2 1.4.3 2017-12-11
## rjson 0.2.15 2014-11-03
## rlang 0.1.6 2017-12-21
## rmarkdown 1.8 2017-11-17
## RMySQL 0.10.13 2017-08-14
## rpart 4.1-12 2018-01-12
## rprojroot 1.3-2 2018-01-03
## Rsamtools 1.30.0 2017-10-31
## RSQLite 2.0 2017-06-19
## rstudioapi 0.7 2017-09-07
## rtracklayer 1.38.3 2018-01-23
## rvest 0.3.2 2016-06-17
## S4Vectors * 0.16.0 2017-10-31
## scales 0.5.0 2017-08-24
## shape 1.4.3 2017-08-16
## shiny 1.0.5 2017-08-23
## SparseM * 1.77 2017-04-23
## splines 3.4.3 2017-12-07
## stats * 3.4.3 2017-12-07
## stats4 * 3.4.3 2017-12-07
## stringi 1.1.6 2017-11-17
## stringr * 1.2.0 2017-02-18
## SummarizedExperiment * 1.8.1 2017-12-19
## survival 2.41-3 2017-04-04
## tibble * 1.4.2 2018-01-22
## tidyr * 0.8.0 2018-01-29
## tidyverse * 1.2.1 2017-11-14
## tools 3.4.3 2017-12-07
## tximport 1.6.0 2017-10-31
## utils * 3.4.3 2017-12-07
## viridis 0.4.1 2018-01-08
## viridisLite 0.2.0 2017-03-24
## vsn 3.46.0 2017-10-31
## withr 2.1.1 2017-12-19
## XML 3.98-1.9 2017-06-19
## xml2 1.2.0 2018-01-24
## xtable 1.8-2 2016-02-05
## XVector 0.18.0 2017-10-31
## yaml 2.1.16 2017-12-12
## zlibbioc 1.24.0 2017-10-31
## source
## cran (@1.4.1)
## cran (@1.56.0)
## cran (@1.48.0)
## Bioconductor
## Bioconductor
## cran (@1.2.0)
## cran (@2.10.1)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## local
## CRAN (R 3.4.0)
## Github (steinbaugh/basejump@265d3ce)
## Github (hbc/bcbioBase@dc61e83)
## Github (hbc/bcbioRNASeq@f44ad74)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## Bioconductor
## Bioconductor
## Bioconductor
## cran (@1.12.0)
## cran (@2.34.2)
## cran (@2.46.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## cran (@1.8.5)
## cran (@0.4.3)
## CRAN (R 3.4.2)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## local
## cran (@1.17.1)
## cran (@1.42.0)
## cran (@0.9.2)
## CRAN (R 3.4.1)
## CRAN (R 3.4.3)
## cran (@1.10.4-)
## local
## CRAN (R 3.4.0)
## cran (@1.14.1)
## cran (@0.4.1)
## cran (@0.3.3)
## cran (@1.18.1)
## CRAN (R 3.4.2)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## cran (@3.20.7)
## cran (@2.2.0)
## CRAN (R 3.4.1)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## cran (@1.5)
## cran (@1.2-2)
## Bioconductor
## Bioconductor
## cran (@1.14.0)
## Bioconductor
## cran (@1.14.1)
## cran (@1.30.1)
## cran (@1.30.1)
## cran (@0.1.6)
## CRAN (R 3.4.0)
## cran (@0.7.0)
## cran (@0.0.12)
## CRAN (R 3.4.2)
## local
## local
## local
## cran (@2.3)
## cran (@0.9.5)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## cran (@4.1-1)
## CRAN (R 3.4.3)
## cran (@1.11.2)
## CRAN (R 3.4.0)
## cran (@1.0)
## cran (@1.3.5)
## CRAN (R 3.4.1)
## cran (@1.16.0)
## Bioconductor
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## cran (@0.6-28)
## CRAN (R 3.4.2)
## cran (@3.34.6)
## CRAN (R 3.4.0)
## cran (@0.7-103)
## CRAN (R 3.4.2)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## cran (@0.9.6)
## cran (@0.4-1)
## cran (@0.53.0)
## CRAN (R 3.4.0)
## local
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.1)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## cran (@1.1-1)
## local
## cran (@1.0.8)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## cran (@1.40.0)
## cran (@1.0.2)
## cran (@1.1.2)
## cran (@1.10.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## cran (@5.34)
## cran (@1.7.1)
## cran (@1.21.0)
## cran (@2.6.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## cran (@0.8.7)
## CRAN (R 3.4.3)
## cran (@0.2.15)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## cran (@0.10.13)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## cran (@1.30.0)
## CRAN (R 3.4.1)
## CRAN (R 3.4.1)
## cran (@1.38.3)
## CRAN (R 3.4.0)
## Bioconductor
## CRAN (R 3.4.1)
## cran (@1.4.3)
## cran (@1.0.5)
## cran (@1.77)
## local
## local
## local
## CRAN (R 3.4.2)
## CRAN (R 3.4.0)
## cran (@1.8.1)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## local
## cran (@1.6.0)
## local
## cran (@0.4.1)
## CRAN (R 3.4.0)
## cran (@3.46.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.1)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## cran (@0.18.0)
## CRAN (R 3.4.3)
## cran (@1.24.0)> mdHeader("`utils::sessionInfo()`", level = 2)utils::sessionInfo()
> sessionInfo()## R version 3.4.3 (2017-11-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] bindrcpp_0.2 forcats_0.2.0
## [3] stringr_1.2.0 dplyr_0.7.4
## [5] purrr_0.2.4 readr_1.1.1
## [7] tidyr_0.8.0 tibble_1.4.2
## [9] ggplot2_2.2.1 tidyverse_1.2.1
## [11] knitr_1.19 bcbioRNASeq_0.1.4
## [13] DEGreport_1.14.1 quantreg_5.34
## [15] SparseM_1.77 bcbioBase_0.0.3
## [17] DESeq2_1.18.1 SummarizedExperiment_1.8.1
## [19] DelayedArray_0.4.1 matrixStats_0.53.0
## [21] Biobase_2.38.0 GenomicRanges_1.30.1
## [23] GenomeInfoDb_1.14.0 IRanges_2.12.0
## [25] S4Vectors_0.16.0 BiocGenerics_0.24.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.0.0 backports_1.1.2
## [3] circlize_0.4.3 Hmisc_4.1-1
## [5] AnnotationHub_2.10.1 plyr_1.8.4
## [7] ConsensusClusterPlus_1.42.0 lazyeval_0.2.1
## [9] splines_3.4.3 BiocParallel_1.12.0
## [11] digest_0.6.15 BiocInstaller_1.28.0
## [13] ensembldb_2.2.0 htmltools_0.3.6
## [15] viridis_0.4.1 magrittr_1.5
## [17] checkmate_1.8.5 memoise_1.1.0
## [19] cluster_2.0.6 limma_3.34.6
## [21] ComplexHeatmap_1.17.1 Biostrings_2.46.0
## [23] annotate_1.56.1 Nozzle.R1_1.1-1
## [25] modelr_0.1.1 R.utils_2.6.0
## [27] prettyunits_1.0.2 colorspace_1.3-2
## [29] rvest_0.3.2 blob_1.1.0
## [31] ggrepel_0.7.0 haven_1.1.1
## [33] crayon_1.3.4 jsonlite_1.5
## [35] tximport_1.6.0 RCurl_1.95-4.10
## [37] genefilter_1.60.0 bindr_0.1
## [39] survival_2.41-3 glue_1.2.0
## [41] gtable_0.2.0 zlibbioc_1.24.0
## [43] XVector_0.18.0 MatrixModels_0.4-1
## [45] GetoptLong_0.1.6 shape_1.4.3
## [47] scales_0.5.0 vsn_3.46.0
## [49] pheatmap_1.0.8 DBI_0.7
## [51] edgeR_3.20.7 Rcpp_0.12.15
## [53] viridisLite_0.2.0 xtable_1.8-2
## [55] progress_1.1.2 htmlTable_1.11.2
## [57] foreign_0.8-69 bit_1.1-12
## [59] preprocessCore_1.40.0 Formula_1.2-2
## [61] htmlwidgets_1.0 httr_1.3.1
## [63] RColorBrewer_1.1-2 acepack_1.4.1
## [65] reshape_0.8.7 pkgconfig_2.0.1
## [67] XML_3.98-1.9 R.methodsS3_1.7.1
## [69] nnet_7.3-12 locfit_1.5-9.1
## [71] labeling_0.3 reshape2_1.4.3
## [73] rlang_0.1.6 AnnotationDbi_1.40.0
## [75] munsell_0.4.3 cellranger_1.1.0
## [77] tools_3.4.3 cli_1.0.0
## [79] RSQLite_2.0 devtools_1.13.4
## [81] broom_0.4.3 evaluate_0.10.1
## [83] yaml_2.1.16 bit64_0.9-7
## [85] AnnotationFilter_1.2.0 nlme_3.1-131
## [87] mime_0.5 formatR_1.5
## [89] R.oo_1.21.0 grr_0.9.5
## [91] xml2_1.2.0 biomaRt_2.34.2
## [93] compiler_3.4.3 rstudioapi_0.7
## [95] curl_3.1 interactiveDisplayBase_1.16.0
## [97] affyio_1.48.0 geneplotter_1.56.0
## [99] stringi_1.1.6 highr_0.6
## [101] GenomicFeatures_1.30.1 lattice_0.20-35
## [103] ProtGenerics_1.10.0 Matrix_1.2-12
## [105] psych_1.7.8 pillar_1.1.0
## [107] GlobalOptions_0.0.12 data.table_1.10.4-3
## [109] cowplot_0.9.2 bitops_1.0-6
## [111] Matrix.utils_0.9.6 httpuv_1.3.5
## [113] rtracklayer_1.38.3 affy_1.56.0
## [115] R6_2.2.2 latticeExtra_0.6-28
## [117] RMySQL_0.10.13 gridExtra_2.3
## [119] codetools_0.2-15 assertthat_0.2.0
## [121] rprojroot_1.3-2 rjson_0.2.15
## [123] withr_2.1.1 mnormt_1.5-5
## [125] GenomicAlignments_1.14.1 Rsamtools_1.30.0
## [127] GenomeInfoDbData_1.0.0 hms_0.4.1
## [129] grid_3.4.3 rpart_4.1-12
## [131] rmarkdown_1.8 dendsort_0.3.3
## [133] logging_0.7-103 lubridate_1.7.1
## [135] shiny_1.0.5 base64enc_0.1-3
## [137] basejump_0.2.0> mdHeader("YAML params", level = 2)YAML params
> print(params)## $bcbFile
## [1] "data/bcb_sub_lung.rda"
##
## $design
## ~prepBatch + sampleclass
## <environment: 0x7fdd1526ea30>
##
## $contrast
## [1] "sampleclass" "lung_distaltumor" "lung_notumor"
##
## $alpha
## [1] 0.05
##
## $lfc
## [1] 1
##
## $outputDir
## [1] "."Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” J. R. Stat. Soc. Series B Stat. Methodol. 57 (1). [Royal Statistical Society, Wiley]: 289–300. http://www.jstor.org/stable/2346101.
Cui, Xiangqin, and Gary A Churchill. 2003. “Statistical Tests for Differential Expression in cDNA Microarray Experiments.” Genome Biol. 4 (4): 210. https://www.ncbi.nlm.nih.gov/pubmed/12702200.
Kolde, Raivo. 2015. Pheatmap: Pretty Heatmaps. https://CRAN.R-project.org/package=pheatmap.
Li, Wentian, Jan Freudenberg, Young Ju Suh, and Yaning Yang. 2014. “Using Volcano Plots and Regularized-Chi Statistics in Genetic Association Studies.” Comput. Biol. Chem. 48 (February): 77–83. doi:10.1016/j.compbiolchem.2013.02.003.
Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2.” Genome Biol. 15 (12): 550. doi:10.1186/s13059-014-0550-8.
Patro, Rob, Geet Duggal, Michael I Love, Rafael A Irizarry, and Carl Kingsford. 2017. “Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression.” Nat. Methods 14 (4): 417–19. doi:10.1038/nmeth.4197.
Soneson, Charlotte, Michael I Love, and Mark D Robinson. 2016. “Differential Analyses for RNA-seq: Transcript-Level Estimates Improve Gene-Level Inferences.” F1000Res. 4 (December). doi:10.12688/f1000research.7563.1.
Ward, Joe H, Jr. 1963. “Hierarchical Grouping to Optimize an Objective Function.” Journal of the American Statistical Association 58 (301). Taylor & Francis: 236–44. doi:10.1080/01621459.1963.10500845.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Use R. Springer New York. doi:10.1007/978-0-387-98141-3.
Yang, Yee Hwa, Sandrine Dudoit, Percy Luu, David M Lin, Vivian Peng, John Ngai, and Terence P Speed. 2002. “Normalization for cDNA Microarray Data: A Robust Composite Method Addressing Single and Multiple Slide Systematic Variation.” Nucleic Acids Res. 30 (4): e15. https://www.ncbi.nlm.nih.gov/pubmed/11842121.